distribution of metal contamination and grain size in the ... · analysis of surface sediments the...

Received: 2 April 2020 /Accepted: 5 July 2020 /Published online: 9 July 2020# The Author(s) 2020

Environ Monit Assess (2020) 192: 502https://doi.org/10.1007/s10661-020-08475-z

Distribution of metal contamination and grain sizein the sediments of Nakdong River, Korea

Shin Kim & Deuk Seok Yang & Yong Seok Kim

Abstract To assess distribution of metal contaminationand grain size in the sediments of Nakdong River (SouthKorea), surface sediments were collected from 21 sitesand analyzed. Within the study area, sand was typicallythe dominant grain size. However, because of the re-duced flow rate and flow velocity, sites adjacent to weirswere composed of relatively fine sediments. A compar-ison of sediment metal concentrations with sedimentquality guidelines proposed by the USA, Canada, andSouth Korea revealed that sites adjacent to weirs hadconcentrations that exceeded the standard values. Theenrichment factor, index of geo-accumulation, and pol-lution load index calculation results that the sites adja-cent to weirs showed high contamination, with Cd ac-counting for the highest contamination levels. Themetals in the study area varies due to the effect of finesediments; therefore, high concentrations of metals ac-cumulated adjacent to weirs where fine sediments weredistributed in greater proportions. Furthermore, Cd ex-hibited the greatest contribution to metal contaminationin the study area and the highest contamination levels

were found at NS19 (adjacent to the Haman weir). Thus,the accumulation of fine sediment increased due to theinfluence of the weirs, thereby increasing the overallamount of metal contamination.

Keywords Nakdong River . Sediment . Grain size .

Metal contamination

Introduction

A river is classified into a mainstream and tributaries,both of which are directly related to human activities. Inrecent years, the natural purification capability of rivershas decreased and the river environment has graduallydeteriorated due to increased residential and industrialsewage and wastewater linked to population growth,improved living standards, and industrial progress(Kim et al. 2015a, b). Furthermore, river environmentsare largely affected by artificial structures, such as weirs,as well as cities and industrial complexes built adjacentto rivers (Ahn et al. 2014).

Contaminants flowing into a river are discharged tothe environment through various paths. Contaminantsflowing into the water system typically accumulate insediments transported and deposited by the flow ofwater, waves, tidal currents, and wind. Contaminantsdeposited in rivers or lakes then accumulate on thebottom and influence the river ecosystem. To obtain acomplete understanding of the river environment, it isimportant to analyze the geochemical components (e.g.,metals) that accumulate in the sediments as well as the

S. Kim (*) :D. S. Yang :Y. S. KimNakdong River Environment Research Center, National Instituteof Environmental Research, 24-11, Gukgasandan-daero 52-gil,Guji-myeon, Dalseong-gun, Daegu, Republic of Koreae-mail: [email protected]

D. S. Yange-mail: [email protected]

Y. S. Kime-mail: [email protected]

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502 Page 2 of 15 Environ Monit Assess (2020) 192: 502

water quality environment (Thornton 1983). Waterquality analysis is key for understanding the currentshort-term environmental conditions; however, sedi-ments contain higher metal concentrations and exhibitsmaller temporal and spatial changes than waster be-cause of their limited movement. Therefore, river sedi-ments are a useful tool for evaluating continuous envi-ronmental effects (Ra et al. 2013). Specifically, metalsin sediments always exist in the aquatic environmentand have an important influence on benthic organisms.Moreover, they have harmful effects on the hydro-ecosystem when released into the water and can leadto physical/chemical changes (Alloway et al. 1988;Dekov et al. 1997). Thus, determining the distributionand behavior of chemical components in sediments,including metals, reveals the sediment environment ofa river and can be used to provide efficient responsemeasures, such as controls on various environmentalfactors (Kim et al. 2001).

Previous studies on river sediments in South Koreahave involved various analysis methods and evaluationsof organic matter and heavy metals inside the sediments,with a particular focus on core and surface sediments (Kimet al. 2010; Park et al. 2011; Kim et al. 2015a, b). Inaddition, researchers have evaluated contamination levelsusing the concentration of metals distributed in the sedi-ments. These studies typically assess the contaminationlevel by comparing with the absolute baseline values ofsediment quality guidelines (SQGs) derived for each coun-try. Another typical assessment method employs the con-centration of metals among crustal materials or in anuncontaminated area as the background concentration forcomparison (Sekabira et al. 2010; Kim and Um 2013; Mdet al. 2015; Han et al. 2016).

Unlike conventional studies that are performed with-in a limited spatial area, this study aims to investigate thecomplete sediment environment of the Nakdong Riverby determining the grain size distribution and metalcontamination in the mainstream and tributaries of theNakdong River system.Moreover, to assess the contam-ination level of metals, a comparison is made not onlywith the SQGs of South Korea but also with those ofUSA and Canada. Based on the measured metal con-centrations, we calculate the EF (enrichment factor),Igeo (index of geo-accumulation), and PLI (pollutionload index). Then, by assessing metal contamination ateach site, we determine the area with the highest con-tamination for further analysis. Furthermore, the princi-pal component analysis (PCA) method is used to

determine the relationships between each variable andthe main factors influencing the contamination level ofsediments in the study area. As a result, the objective ofthis study is to understand distribution of metal contam-ination and grain size in sediments and provide usefuldata for future management and contamination assess-ments of river sediments.

Material and methods

Study area and sediment sampling

The study area, i.e., the Nakdong River in South Korea,has the basin area of 23,384.21 km2, a mainstream riverlength of 400.7 km, and a river length of 510.36 km. It islocated in the southeast of South Korea at a longitude andlatitude of 127° 29′ 19″–129° 18′ 00″ and 34° 59′ 41″–37°12′ 52″. It lies adjacent to the Han River basin to the northand the Geum River and Seomjin River basins to the west(Jung et al. 2016). The Taebaek Mountains form the eastsea coast basin andwatersheds in the east, and the southernsea area of the Nakdong River lies to the south. Theadministrative areas include three metropolitan cities (Bu-san, Daegu, and Ulsan Metropolitan Cities) and parts offive provinces (Gyeongsangnam-do, Gyeongsangbuk-do,Jeonlanam-do, Jeonlabuk-do, and Gangwon-do). Further-more, during the Four River Refurbishment Project, whichwas conducted in order to control floods and secure waterresources that were deemed insufficient for the continu-ously increasing water demands of recent years, riverchannels were dredged and a total of 16 multi-purposeweirs were constructed. Of these, eight were built in theNakdong River (NIER 2017).

This study selected a total of 21 sites in the main-stream and tributaries of the Nakdong River and collect-ed surface sediments from September to November2016. For the sediment samples, a Ponar grab was used,a type of gravity corer in which the bottom blade closeswhen the corer touches a basal surface, releasing thetension. Surface sediments were collected in this wayfrom the upper 1–3 cm of sediments. Among the 21sites, 11 are located in the mainstream and 10 in thetributaries. Eight of the mainstream sites (NS04, NS07,NS08, NS09, NS10, NS11, NS12, and NS19) are adja-cent to weirs (Sangju Weir, Nakdan Weir, Gumi Weir,Chilgok Weir, Gangjeong-Goryeong Weir, DalseongWe i r , Hapcheon -Changnyeong We i r , a ndChangnyeong-Haman Weir, respectively) (Fig. 1).

Analysis of surface sediments

The samples used for grain size analysis were firstcollected in plastic bottles and then transported to thelaboratory for analysis. For the grain size analysis, a pre-

treatment process was performed, which decomposedthe organic matter with hydrogen peroxide (H2O2).Subsequently, the grain size was measured by aMicrotrac S3500 grain size analyzer that uses the laserdiffraction principle and calculates the grain size

Fig. 1 Location of sampling sites in the study area

Environ Monit Assess (2020) 192: 502 Page 3 of 15 502


distribution by measuring the difference in diffractionpatterns when the sediment grains pass through a laserbeam. The measured results were classified into sand,silt, and clay according to the sediment composition.The sand was sub-divided into five sizes: very coarsesand, coarse sand, medium sand, fine sand, and very finesand. The textural parameters for the sediments, such asmean grain size, sorting, and skewness, were obtainedusing the method of Folk and Ward (1957) afterconverting the weight percentage in each grain size classof sediments. To express the grade of grain size, becausethe logarithmic scale is more useful than theequimultiple scale, Φ (phi) was used, and the relationbetween Φ and grain diameter, D, was expressed as Φ =− log2 D (size in mm).

Samples for metal analysis (Al, Li, Zn, Cr, Pb, Ni,Cu, and Cd) in sediments were first mixed onsite at thetime of collection using a non-metallic sample spoonand then sieved with a 150-μm sieve. Subsequently,samples were placed in glass bottles and stored in aportable cool box and were then transported to thelaboratory for analysis. They were then dried in a naturalstate and crushed to obtain ground powder samples. Thepowder samples were pre-treated by adding HNO3,HClO4, and HF (in a 2:1:2 ratio), and the analysis wasperformed by an inductively coupled plasma–atomicemission spectrometry (ICP-AES). This analysis meth-od was performed in accordance with the “test standardfor sediments of river and lake” one of the “water qualitypollution test standards” of the National Institute ofEnvironmental Research of South Korea (Ministry ofEnvironment 2012).

Sediment quality guidelines

In this study, the metal analysis results were comparedwith the sediment quality guidelines of USA, Canada,and South Korea (Table 1). The US EPA sedimentquality standard is a standard of the Regional Environ-mental Protection Agency for freshwater sediment con-tamination classification. According to the content ofeach metal element (Zn, Pb, Cu, Ni, and Cd), there arethree standard classifications: non-polluted, moderatelypolluted, and heavily polluted (US EPA 1999).Canada’s Ontario sediment guidelines express the ad-verse effects of sediments on benthic organisms in termsof probability, with three contamination levels: NELindicates no effect on the organisms living in the sedi-ments, LEL indicates no effect on a large number of

organisms living in the sediments, and SEL indicates anadverse effect on benthic organisms (CCME 1995). TheNational Institute Environmental Research sedimentpollution evaluation of South Korea provides four levelsof classification for the effects of sediment metal contenton benthic organisms. Level I indicates almost no pos-sibility of toxicity appearing in the benthic organisms.Level II indicates a possibility of toxicity, level IIIindicates a relatively high possibility of toxicity, andlevel IV indicates a very high possibility of toxicity(NIER 2015).

Calculation methods of EF, Igeo, and PLI

To evaluate metal contamination in sediments, compar-isons are typically made with the metal contents ofcrustal material or natural concentrations in an uncon-taminated area near the study area. These methods canestimate the concentrated amount of measured metalcontents by using the difference or proportion ofestablished standard elements and natural metal contents(Kim and Jang 2014). Accordingly, to eliminate theeffect of grain size in the sediments, this study usedAl, which is a major element in the sediments andexhibits relatively small content variations and a largedistribution in the sediments. For the background con-centrations, we used the values provided by the RiverSediments Background Concentrations of the NIER ofSouth Korea (NIER 2011), which are average concen-trations of metals distributed in South Korean riversediments, considering the local characteristics of thestudy sites. The contamination level was then assessedby calculating the EF, Igeo, and PLI. The EF is calcu-lated by Eq. (1):

EF ¼ M metalð ÞM referenceð Þ

� �sediment =

M metalð ÞM referenceð Þ

� �reference value ð1Þ

where (M(metal) / M(reference)) sediment is the con-centration ratio of the metal to the reference element(Al) in the sediment sample, and where (M(metal) /M(reference)) reference value refers to the ratio of themetal to the reference element in the background con-centration (Bruland et al. 1974). An EF value of 1.5 orless indicates an uncontaminated natural environment,whereas EF greater than 1.5 implies artificial contami-nation due to inflow through the air and river (Zhangand Liu 2002; Hyun et al. 2007). And Yongming et al.(2006) suggest deficiency to minimal metal enrichmentwhen EF values are lower than 2.


The Igeo shows the relative concentration level ofmetals. Unlike the load coefficient, it can be used toevaluate the level of contamination by directly gradingthe contamination level of sediments. Igeo = 0–1 indi-cates unpolluted/moderately polluted, 1–2 indicatesmoderately polluted, 2–3 indicates moderatelypolluted/strongly polluted, and > 3 indicates stronglypolluted. Igeo is calculated by Eq. (2), where M(sedi-ment) is the corresponding metal concentration,M(background) is the background concentration of thecorresponding metal, and a constant of 1.5 is multipliedfor compensation (Muller 1979).

Igeo ¼ log2M sedimentð Þ

M backgroundð Þ � 1:5 ð2Þ

The PLI can assess the overall contamination in anarea, including all analyzed metals, unlike Igeo and EF.It is calculated by Eq. (3), calculated as a ratio of themetal concentration, M(sediment), and the backgroundconcentration of the natural environment state M(back-ground). When PLI is higher than 1, it implies contam-ination. As the value increases, the contamination levelincreases (Tomlinson et al. 1980).

PLI

¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiM sedimentð ÞM referenceð Þ

� �1� M sedimentð Þ

M referenceð Þ� �

2�… M sedimentð ÞM referenceð Þ

� �n

n

s

ð3Þ

Principal component analysis

Principal component analysis is a statistical analysismethod that groups multiple variables with similar com-mon dimensions and reduces them to a relatively small

number of factors (Grant 1990). This is the most com-monmultivariate statistical method used in environmen-tal studies to reduce data and extract a small number oflatent factors for analyzing relations among the ob-served variables (Chen et al. 2007). To investigate therelationship between all factors analyzed in this study,grain size analysis (i.e., sediment composition, meangrain size, sorting, and skewness), analysis of the con-centration and contamination (i.e., EF, Igeo, and PLI) ofmetals in the study area, and principal component anal-ysis were conducted using SPSS 20.0 software.

Result and discussion

Distribution of grain size

According to the grain size analysis results of surfacesediments in the study area, sand is the dominant grainsize overall (average 88.02%). At the majority of sites,the sand content exceeds 90% with medium sand themost dominant class (average 34.96%). At sites NS04(61.7%), NS09 (69.1%), and NS10 (71.9%), the areasadjacent to weirs constructed in the study area, slightlysmaller sand contents are observed compared with othersites. Specifically, site NS07 is predominantly com-posed of fine-grained sediments, with 27.5% sand,60.2% silt, and 12.1% clay (Fig. 2). Grain size is signif-icantly influenced by the flow rate and flow velocity(Kim et al. 2017). Indeed, flow velocity changes andtemporary stagnation of flowing streams are linked tothe riverbed slope and river channel direction changescaused by the installation of artificial structures (Ohet al. 2003). This agrees with our results, whereby finer

Table 1 US EPA sediment quality standard, Ontario sediment quality guidelines and NIER sediment pollution evaluation standard (unit:mg/kg)

US EPA sediment quality standard Ontario sediment qualityguidelines

NIER sediment pollution evaluationstandard

Non-polluted Moderately polluted Heavily polluted LEL SEL I II III IV

Zn < 90 90–200 > 200 120 820 ≤ 363 ≤ 1170 ≤ 13,000 > 13,000Pb < 40 40–60 > 60 31 250 ≤ 59 ≤ 154 ≤ 459 > 459Cu < 25 25–50 > 50 16 110 ≤ 48 ≤ 228 ≤ 1890 > 1890Cr - - - - - ≤ 112 ≤ 224 ≤ 991 ≤ 991Ni < 20 20–50 > 50 16 75 ≤ 40 ≤ 87.5 ≤ 330 > 330Cd - - > 8.0 0.6 10 ≤ 0.4 ≤ 1.87 ≤ 6.09 > 6.09

grain sizes are found at sites adjacent to weirs than atother sites because of the effect of decreased flow rateand flow velocity close to the weirs.

The mean grain size is 1.80 Φ, which corresponds tomedium sand, and the sand grains are predominantlydistributed in the ranges of 0–1 Φ (coarse sand) and 1–2Φ (medium sand). However, at sites NS04 and NS07,where silt is dominant, the mean grain size is 3.43 Φ and5.44 Φ, respectively, which are finest grain sizes in thestudy area. Sorting indicates the homogeneity level ofgrains through a standard deviation of the grains. A valueof less than 0.35 indicates verywell sorted grains, 0.35–0.5Φ indicates well sorted, 0.5–1 Φ indicates moderately wellsorted, 0.7–1.0 Φ indicates moderately sorted, 1–2 Φ indi-cates poorly sorted, 2–4Φ indicates very poorly sorted, andabove 4 Φ indicates extremely poorly sorted. The averagevalue for the study area is 1.40 Φ, which corresponds topoorly sorted grains. In general, grains are moderatedsorted and poorly sorted, whereas sites NS04, NS09, andNS10 correspond to very poorly sorted grains. Skewnessshows the asymmetry of grain distributions and indicatesthe existence or non-existence of coarse and fine fractions.As the skewness approaches 0, coarse and fine sedimentsare distributed more symmetrically. The average value is0.16 for the study area, and positive values are observed atall sites except for six that exhibit negative values (Fig. 3).

The textural parameters derived from the grainsize analysis reflect the energy conditions in thesedimentation environment. In a low-energy envi-ronment, the sediments are fine, sorting is poor,

and positive skewness is shown. Conversely, in ahigh-energy environment, coarse sediments withgood sorting tend to show positive or negativeskewness (Kim et al. 2018). Furthermore, a largepositive value of skewness indicates dominant ero-sion due to physical effects such as flow velocity.On the other hand, when the skewness is small,the area can be classified as one where sedimen-tation is dominant because the physical effects arerelatively weak (Folk 1980). In other words, thevery high silt content adjacent to the weirs in thestudy area, as well as the poor sorting and lowskewness, indicates a reduced flow rate and flowvelocity, which can have large effects on the trans-port of sediments, thereby indicating a low-energyenvironment where sedimentation is active.

Contamination and concentration of metal

The concentrations of the eight analyzed metals (Al, Li,Zn, Pb, Cu, Cr, Ni, and Cd) are shown in Table 2. Theaverage concentration of Al is 6.9% and exceeds 8% atfour sites. The average concentration of Li is 25.6mg/kg,exceeding 40 mg/kg at three sites. The average concen-tration of Zn is 120.2 mg/kg and exceeds 200 mg/kg atNS09 and NS19. The average concentration of Cr is48.5 mg/kg and is highest at site NS19 (83.6 mg/kg).The average concentrations of Pb and Cu are 24.6mg/kgand 16.9 mg/kg, respectively. The highest concentra-tions of Pb (over 40 mg/kg) and Cu (43.3. mg/kg) are

Fig. 2 Sediment composition of surface sediments in the study area


Fig. 3 Mean grain size, sorting, and skewness of surface sediments in the study area


observed at site NS19, respectively. The average con-centrations of Ni and Cd are 16.08 mg/kg and0.38 mg/kg, respectively, and their maximum concen-tration is observed at site NS19.

Table 3 presents the comparison of the sediment metalconcentration results of the study area (i.e., NakdongRiver) with the results of Korea’s other three majorrivers: Han River, Geum River, and Yeongsan River.As shown in the table, the metal concentrations in thesediments of the Nakdong River, i.e., all study sites forthis investigation, showed similar results as the metalconcentrations observed in the Geum River (Lee et al.2014) and Yeongsan River (Shin et al. 2015). However,sediments of the Han River, which lies more closelyadjacent to urban areas in comparison with the otherrivers, showed relatively high metal concentrations, in-cluding Pb and Cu, demonstrating high pollution by

these contaminants (Lai et al. 2013). Additionally, metalconcentrations of river sediments found in other coun-tries, such as China, India, and Australia, generallyshowed higher concentration distributions in comparisonwith this study area. In particular, the Ganga River ofIndia can be seen to have significantly higher concentra-tions in comparison with the Nakdong River due to theeffects of domestic, industrial, and agricultural untreatedwastewater (Song et al. 2011; Duodu et al. 2016; Pandeyet al. 2014). As such, it can be inferred that the sedimentsfound in the Nakdong River showed lower metal con-centrations in comparison with other river sediments dueto the lesser amount of anthropogenic impacts due todomestic, industrial, and agricultural environmentalfactors.

Table 4 shows the comparison of metal concentra-tions in the study area with the US EPA sediment quality

Table 2 Metal concentrations of surface sediments in the study area

Al (%) Li (mg/kg) Zn (mg/kg) Cr (mg/kg) Pb (mg/kg) Cu (mg/kg) Ni (mg/kg) Cd (mg/kg)

NS01 7.98 12.0 160.0 11.6 14.8 11.4 8.0 0.72

NS02 7.29 22.2 82.2 12.4 23.0 10.5 10.2 0.12

NS03 4.62 21.9 64.3 36.3 20.8 9.2 12.6 0.17

NS04 7.70 29.2 118.5 44.3 25.4 37.9 17.5 0.36

NS05 4.79 11.6 67.0 29.5 18.2 9.5 10.0 0.19

NS06 4.74 20.7 49.0 52.1 18.0 8.5 11.6 0.32

NS07 6.76 42.3 145.3 55.8 30.4 25.3 27.0 0.39

NS08 7.34 36.0 137.1 50.5 27.2 19.7 20.9 0.39

NS09 6.39 42.1 217.1 51.8 31.9 29.4 28.3 0.42

NS10 8.54 30.9 120.4 50.5 26.6 18.2 19.0 0.46

NS11 4.17 17.2 81.6 62.4 23.8 13.7 11.2 0.53

NS12 5.23 19.3 75.6 49.2 22.5 10.9 12.1 0.52

NS13 8.14 21.2 87.6 23.6 22.5 8.5 4.8 0.26

NS14 7.13 28.6 151.4 29.2 23.8 13.9 8.2 0.22

NS15 5.71 24.7 120.4 62.6 22.3 13.3 15.7 0.36

NS16 8.30 17.8 86.0 72.5 17.6 10.2 17.5 0.34

NS17 8.06 25.6 127.7 79.9 24.4 20.7 26.3 0.34

NS18 7.17 19.6 62.1 61.6 18.7 9.8 13.9 0.29

NS19 8.25 43.9 213.1 83.6 35.5 43.3 32.4 0.51

NS20 6.97 22.4 95.9 56.9 25.6 8.5 13.5 0.45

NS21 7.61 26.7 126.7 28.0 28.0 12.2 15.3 0.40


standards, Canada’s Ontario sediment guidelines, andSouth Korea’s NIER sediment pollution evaluations. Ac-cording to the US EPA sediment quality standard, ten andtwo sites are moderately polluted with Zn and Pb, respec-tively. Moreover, sites NS09 and NS09 are heavily pollut-ed for Zn and Pb, respectively. With respect to Cu and Ni,no sites are heavily polluted, but six and ten sites aremoderately polluted, respectively. According to the Ontar-io sediment guidelines, one, five, and seven sites corre-spond to LEL for Zn, Pb, and Cu, respectively. Further-more, for Cr, all sites excluding the four positions corre-sponding to NEL correspond to LEL. For Ni, 13 sitescorrespond to LEL. For Cd, all sites excluding NS01correspond to NEL. According to the NIER sedimentpollution evaluation, the majority of positions correspondto level I, which is an unpolluted level. However, at siteNS09, Pb corresponds to level II, and at four sites (NS01,NS07, and NS08), Cd corresponds to level II, which is thehighest contamination level among all the metals. In thecomparative analysis with the SQGs of three countries, themajority of metal contents indicate high contaminationlevels at sites adjacent to weirs.

The EF calculation of surface sediments in the studyarea reveals Pb, Zn, Cu, Cr, and Ni values of less than1.5 at all sites, indicating no anthropogenic contamina-tion and minimal metal enrichment. However, Zn rela-tively higher contamination at site NS09 (1.30), adjacentto the Chilgok Weir. Moreover, Cr values are relativelyhigher at four sites, and Cd, the metal with the highestEF values, exhibits values over 1.5 at all except NS01,NS12, and NS13(adjacent to the weir), indicating thatanthropogenic contamination is pervasive. These resultsimply that, in general, contamination is relatively highand large impacts in weir-constructed areas. Among allmetals, contamination by Cd is the highest (Fig. 4).

The Igeo value for Cd is 0.26 at site NS01, whichcorresponds to partially unpolluted/moderately polluted.Besides this, no other metals or sites exceed Igeo = 0,indicating that the contamination level is practicallyunpolluted. However, in the calculation results, Zn ex-hibits relatively high values at sites NS09 (− 0.57) andNS22 (− 0.34), and Cu and Cr exhibit relatively highvalues (− 0.60 and − 0.58, respectively) at site NS19.Because these results show similar trends to the EF

Table 3 Comparison of different metal concentrations in surface sediments in this study and other studies

Location Nation Reference Zn (mg/kg) Cr (mg/kg) Pb (mg/kg) Cu (mg/kg) Ni (mg/kg) Cd (mg/kg)

Nakdong River South Korea This study 49.0–217.0 11.6–83.6 14.8–35.5 8.5–43.3 4.8–32.4 0.12–0.72

Han River South Korea Lai et al. (2013) 52.1–690.7 27.3–146.8 17.1–106.2 5.1–158.5 8.8–57.5 0.05–1.32

Geum River South Korea Lee et al. (2014) 57.5–124.9 37.6–78.6 8.3–19.4 11.8–22.4 9.9–20.9 0.05–0.18

Yongsan River South Korea Shin et al. (2015) 22–167 2–72 14–40 2–26 3–35 0.01–0.09

Brisbane River Australia Duodu et al. (2016) 142–257 82–332 25–126 20–110 20–34 0.6–0.9

Chanjiang River China Song et al. (2015) 50.6–221.0 64.5–126.7 19.0–173.2 10.8–87.9 24.2–52.6 0.35–16.45

Ganga River India Pandey et al. (2014) 137.3–201.2 126.8–196.1 148.8–211.4 12.7–84.0 14.6–82.5 9.5–79.0

Table 4 Evaluation for sediment quality guidelines (SQGs) using US EPA, Ontario of Canada, and NIER of Korea

SQGs Site name (metal)

US EPA Non-polluted Other sites (metals)

Moderately polluted NS01, 03, 10, 14, 15 (Zn), NS08 (Zn, Ni), NS07, 08, 09, 17,19 (Zn, Cu, Ni), NS21 (Zn, Pb, Cu)

Heavy polluted N/D

Ontario of Canada NEL Other sites (metals)

LEL NS14, 15 (Zn), NS16 (Ni), NS01 (Zn, Cd), NS03 (Cu, Ni), NS07,08, 10, 17 (Zn, Cu, Ni), NS09, 19, 21 (Zn, Pb, Cu, Ni)

SEL N/D

NIER of Korea I level Other sites (metals)

II level NS01, 09, 10, 19, 20 (Cd)

III level N/D

IV level N/D


results, we confirm that relatively high contaminationappears at sites adjacent to weirs (Fig. 5).

According to the calculation results of EF and Igeovalues, Cd was found to be relatively higher than othermetals, indicating a state of higher pollution. Ganugapentaet al. (2018) previously suggested that the high contami-nation degree of Cd is generally attributed to agriculturalrunoff, industrial activities, and other anthropogenic inputs.Additionally, the official Group of Experts on the Scien-tific Aspects of Marine Pollution (GESAMP) ( 1985)proposed that the contamination degree of Cd may alsoincrease due to influential factors, such as agricultural soils,miningwaste, andmunicipal sewage effluents and sludges.Accordingly, it was assumed that Cd in sediments found inthe NS01 (not adjacent to weirs) showed a relatively highcontamination (EF > 1.5, and Igeo > 0) degree due toanthropogenic impacts. Therefore, additional studiesshould be conducted to explore detailed causes affectingthe Cd concentration levels of sediments observed in theNakdong River.

Unlike the contamination assessments performedusing EF and Igeo, the PLI value can be used toidentify the total contamination level by using theconcentrations of all analyzed metals. All PLI valuesfor all sites in the study area are less than 1, indi-cating significantly low contamination. Furthermore,referring to the findings of one study, which sug-gested that the area with contaminated river sedi-ments (PLI > 1) was influenced by metal pollutantsfrom nearby mines, cities, and industrial activities(Ahn et al. 2019), the PLI value of the present studyarea was shown to be considerably low, indicatingthat for this area, the anthropogenic impacts onmetals induced by nearby areas were relativelysmall. However, PLI values also indicate relativelyhigh contamination at the sites NS04, NS07, NS08,NS09, and NS19, which are located in the weirsections. Among these sites, metal contamination ishighest at site NS19 (PLI = 0.95), located at HamanWeir (Fig. 6).

Principal component analysis

The principal component analysis was performed usingthe EF, Igeo, and PLI calculation results, which wereemployed in the grain size, metal concentration, andcontamination assessment, as the factors analyzed inthis study. As a result, two principal components wereextracted. In the principal analysis results, the eigenval-ue, which indicates how much each factor explains theinformation of existing variables, is 46.08% for factor 1and 15.08% for factor 2.

The factors corresponding to factor 1, which have thelargest effect on the sediment environment of the studyarea, are PLI, Zn, Pb, Cu, and Ni, EF, Igeo, fine sedi-ments such as very fine sand, silt, and clay, and meangrain size (Table 5). In the component diagram thatvisualizes the principal components, Zn, Pb, Cu, andNi, which are the metals that have no effect on metalcontamination, are concentrated on the right-hand sideof the diagram, indicating the assessment result of finesediments corresponding to factor 1, PLI (overall con-tamination of the site), and contamination. Therefore, it

Fig. 4 Enrichment factors of the metals of surface sediments in the study area

Fig. 5 Index of geo-accumulations of metals of surface sediments in the study area


was determined that grain size has a large influence onthese four metals. Furthermore, coarse sediments (verycoarse sand, coarse sand, and medium sand) have a smalleffect on contamination. However, as the concentrations ofCd have a relatively weak relationship with the mainfactor, fine sand, it is determined that Cd has a stronginfluence onmetal contamination in the study area (Fig. 7).

The factors that have large correlation coefficientsgenerally exhibit close relationships with geochemical

or environmental characteristics. As sediment grainsbecome finer, the surface area becomes larger. Conse-quently, more matter can be adsorbed, and the increasein ion exchange facilitates the adsorption of metals andorganic matter (Horowitz 1991). Therefore, we suggestthat the study area is predominantly affected by finesediments. Moreover, because of flow rate and flowvelocity effects, metal concentrations and contaminationare relatively high at sites adjacent to the weirs, which

Fig. 6 Pollution load index of surface sediments in the study area


are composed of fine sediments. And the highest con-tamination levels were found at NS19 (adjacent to theHaman weir). Therefore, structures such as artificiallyconstructed weirs are thought to be the major factor thataffect grain size distribution and control the contamina-tion of metals in the sediments of this study area.

Conclusions

In order to understand the contamination environmentof surface sediments in the entire Nakdong River sys-tem, surface sediments were collected from 21 sites inthe mainstream and tributaries of Nakdong River, South

Table 5 Component matrix (factors 1 and 2) and eigenvalue loading of surface sediments in the study area (principal component analysis)

Units Factor1

Factor2

Units Factor1

Factor2

Units Factor1

Factor2

Units Factor1

Factor2

Grainsize

Very coarsesand

− 0.465 − 0.621 Metals Al 0.291 − 0.575 EF Zn 0.752 − 0.172 Igeo Zn 0.772 − 0.437

Coarse sand − 0.631 − 0.002 Li 0.859 − 0.083 Cr 0.266 0.929 Cr 0.541 0.740Medium sand − 0.145 0.785 Zn 0.777 − 0.392 Pb 0.484 0.444 Pb 0.832 − 0.025Fine sand 0.345 0.224 Cr 0.526 0.690 Cu 0.855 0.021 Cu 0.923 − 0.186Very fine sand 0.726 − 0.110 Pb 0.844 − 0.072 Ni 0.757 0.365 Ni 0.828 0.201Silt 0.736 − 0.275 Cu 0.879 − 0.169 Cd 0.184 0.434 Cd 0.497 0.149Clay 0.657 − 0.257 Ni 0.864 0.112 PLI 0.963 0.072 Eigenvalue

loading46.08% 15.08%

Mz 0.805 − 0.023 Cd 0.395 0.022So 0.785 − 0.334 -Sk 0.420 − 0.296

Fig. 7 Plot of principal component analysis of surface sediments in the study area



Korea. Through a grain size analysis of the collectedsurface sediments, the sediment composition, meangrain size, sorting, and skewness were obtained. More-over, the contamination was assessed by analyzing themetal concentration in the sediments, comparing it withthe SQGs of different countries, and calculating the EF,Igeo, and PLI values.

In the study area, sand was the dominant grain size;yet, sites adjacent to weirs were composed of relativelyfine sediments. This indicated that fine sediments aredeposited due to the reduced flow rate and flow velocityaround weirs. A comparison of the metal concentrationswith the SQGs of USA, Canada, and Korea revealedthat contamination exceeded the standard concentra-tions at sites adjacent to weirs. EF and Igeo values alsoshowed high levels of contamination at sites adjacent toweirs, with Cd contamination the highest. Furthermore,PLI values revealed that the highest contamination of allweir-adjacent sites occurred at site NS19, located atHaman weir.

In conclusion, anthropogenic contamination in sur-face sediments of the study area is extremely low, andfindings revealed that metal contamination levels areinfluenced by the effect of fine sediments. Moreover,metal concentrations tend to accumulate substantially atsites adjacent to weirs, where fine sediments are activelydeposited because of reduced flow rate and flow veloc-ity. Among the metals, Cd concentrations exhibited aweak relationship with fine sediment content and dem-onstrated the greatest effect on metal contamination inthe study area. Furthermore, the highest contaminationby metals occurred at site NS19, which is located adja-cent to the HamanWeir. Thus, the overall accumulationof fine sediments increased due to the influence of weirs,thereby increasing pollution by metal contamination insediments of the study area.

Acknowledgments The Nakdong River Environment ResearchCenter, National Institute of Environmental Research of Korea,provided all necessary support for the research.

Authors’ contributions All authors contributed to the studyconception and design. Material preparation, data collection andanalysis were performed by Yong Seok Kim, Deuk Seok Yang,and Shin Kim. The first draft of the manuscript was written byShin Kim and all authors commented on previous versions of themanuscript. All authors read and approve the final manuscript.

Funding information This research was supported by a grantfrom the National Institute of Environmental Research of Korea(NIER-2019-01-01-078).

Compliance with ethical standards

Conflict of interest The authors declare that they have no con-flict of interest.

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References

Ahn, J. M., Im, T. H., Lee, K. L., Jung, K. Y., Lee, J.W., Cheon, S.U., & Park, I. H. (2014). A study on efficiency of watersupply through conjunctive operation of reservoirs andmulti-function weir in the Nakdong river. Journal ofKorean Society Water Environmental, 30(2), 138–147.https://doi.org/10.15681/KSWE.2014.30.2.138.

Ahn, J. M., Kim, S., & Kim, Y. S. (2019). Selection of prioritymanagement of rivers by assessing heavymetal pollution andecological risk of surface sediments. EnvironmentalGeochemistry and Health. https://doi.org/10.1007/s10653-019-00284-9.

Alloway, B. J., Thornton, I., Smart, G. A., Sherlock, J. C., &Quinn, M. J. (1988). Metal availability. The Science of theTotal Environment, 75, 41–69. https://doi.org/10.1016/0048-9697(88)90159-3.

Bruland, K. W., Bertine, K., Koide, M., & Golberg, E. D. (1974).History of metal pollution in southern California coastalzone. Environmental Science and Technology, 8(5), 425–432. https://doi.org/10.1021/es60090a010.

CCME. (1995). Protocol for the derivation of canadian sedimentquality guidelines for the protection of life (report CCMEEPC-98E). Canada, Ottawa, ht tps: / /www.ccme.ca/files/Resources/supporting_scientific_documents/pn_1176_e.pdf.

Chen, K., Jiao, J. J., Huang, J., & Huang, R. (2007). Multivariatestatistical evaluation of trace elements in groundwater in acoastal area in Shenzhen, China. Environmental Pollution,147, 771–780. https://doi.org/10.1016/j.envpol.2006.09.002.

Dekov, V.M., Komy, Z., Araujo, F., Van Put, A., &Van Grieken, R.(1997). Chemical composition of sediments, suspended matter,river water and ground water of the Nile (Aswan-Sohag tra-verse). The Science of the Total Environment, 201(3), 195–210.https://doi.org/10.1016/S0048-9697(97)84057-0.

Duodu, G. O., Goonetilleke, A., &Ayoko, G. A. (2016). Comparisonof pollution indices for the assessment of heavy metal in

https://doi.org/https://doi.org/10.15681/KSWE.2014.30.2.138https://doi.org/10.1007/s10653-019-00284-9https://doi.org/10.1007/s10653-019-00284-9https://doi.org/10.1016/0048-9697(88)90159-3https://doi.org/10.1016/0048-9697(88)90159-3https://doi.org/10.1021/es60090a010https://www.ccme.ca/files/Resources/supporting_scientific_documents/pn_1176_e.pdfhttps://www.ccme.ca/files/Resources/supporting_scientific_documents/pn_1176_e.pdfhttps://www.ccme.ca/files/Resources/supporting_scientific_documents/pn_1176_e.pdfhttps://doi.org/10.1016/j.envpol.2006.09.002https://doi.org/10.1016/S0048-9697(97)84057-0


Brisbane River sediment. Environmental Pollution, 219, 1077–1091. https://doi.org/10.1016/j.envpol.2016.09.008.

Folk, R. L. (1980). Petrology of sedimentary rock. Texas:Hemphill Publishing Company Austin.

Folk, R. L., & Ward, W. C. (1957). Brazos river bar [Texas]: Astudy in the significance of grain size parameter. Journal ofSedimentary Petrology, 27(1), 3–26. https://doi.org/10.1306/74D70646-2B21-11D7-8648000102X1865D.

Ganugapenta, S., Nadimikeri, J., Chinnapolla, S. R. R. B., Ballari,L., Rajasekhar, M., Madiga, R., Nirmala, K., & Tella, L. P.(2018). Assessment of heavy metal pollution from the sedi-ment of Tupilipalem Coast, southeast coast of India.International Journal of Sediment Research, 33, 294–302.https://doi.org/10.1016/j.ijsec.2018.02.004.

GESAMP (1985). Cadmium, lead and tin in the marine environ-ment, UN/FAO/UNESCO/WHO/WMO/IAEA/UNEP jointGroup of Experts on the Scientific Aspects of MarinePollution, GESAMP Report and Studies No.22.

Grant, A. (1990). Multivariate statistical analysis of sedimentgeochemistry. Marine Pollution Bulletin, 21(6), 297–299.https://doi.org/10.1016/0025-326X(90)90594-X.

Han, G.M., Hong, S. H., Shim,W. J., Ra, G. T., Kim, G. T., Ha, S.Y., Jang, M., & Kim, G. B. (2016). Assessment of persistentorganic and heavy metal contamination in Busan coast:Application of sediment quality index. Ocean and PolarResearch, 38(3), 171–184. https://doi.org/10.4217/OPR.2016.38.3.171.

Horowitz, A. J. (1991). A primer on sediment-trace elementchemistry. Lewis publish, Chelsea, https://pdfs.semanticscholar.org/bar12/56aa9766c0d3e0b6ea73bb1fa9561fbcf120.pdf.

Hyun, S., Lee, C. H., Lee, T., & Choi, J. W. (2007).Anthropogenic contributions to heavy metal distributions inthe surface sediments ofMasan bay, Korea.Marine PollutionBulletin, 54(7), 1059–1068. https://doi.org/10.1016/i.marpolbul.2007.02.013.

Jung, K. Y., Lee, K. L., Im, T. H., Lee, I. J., Kim, S., Han, K. Y., &Ahn, J. M. (2016). Evaluation of water quality for theNakdong river watershed using multivariate analysis.Environmental Technology and Innovation, 5, 67–82.https://doi.org/10.1016/j.eti.2015.12.001.

Kim, J. G., & Jang, H. S. (2014). Evaluation of characteristics ofparticle composition and pollution of heavy metals for bot-tom sediments in Cheonsu bay, Korea-Comparison of thesediments environment of farming area and non-farmingarea. Journal of Korea Society of Marine Environment andSafety, 20(4), 358–371. ht tps: / /doi .org/10.7837/kosomes.2014.20.4.358.

Kim,D.H.,&Um,H.H. (2013). Estimation of the sediment pollutionin coast of Gwangyang, Mokpo and Shinan, Korea. Journal ofKorea Society of Marine Environment and Safety, 19(4), 303–308. https://doi.org/10.7837/kossmes.2013.19.4.303.

Kim, J. Y., Eun, G.Y., Koh, Y.K., Youn, S. T., Oh, K.H., &Kim,D.J. (2001). Sedimentary environments and geochemical charac-ters of the core sediments near Naju-Yeongam area in the lowerpart of the Yeongsan river, Cheonnam, Korea. Journal of theKorean Earth Science Society, 22(4), 301–316.

Kim, M. J., Chang, T. S., Rhee, C. W., & Jin, J. H. (2010).Feasibility assessment of a microtrac tri-laser diffraction par-ticle size analyzer for use in sedimentary geology: A

comparison of grain-size analysis with sieve-pipette method.Journal of the Geological Society of Korea, 46, 197–205.

Kim, S., Kim, J. E., Lee, K. C., Lee, K. Y., Jeon, H. L., Yu, J. J.,Lee, I. J., & Ahn, J. M. (2015a). Distribution and pollution ofheavy metals in surface sediments from Nakdong river.Journal of Environmental Science International, 24(8),969–980. https://doi.org/10.5322/JESI.2015.24.8.969.

Kim, S., Lee, G. Y., Kim, J. U., Lee, K. C., Ahn, J. M., Lee, I. J.,Jung, K. Y., & Im, T. H. (2015b). Vertical variation ofsediment structure and geochemical characteristics of coresediment in Nakdong river midstream. Journal of KoreaSociety on Water Environment, 31(3), 304–312. https://doi.org/10.15681/KSWE.2015.31.3.304.

Kim, S., Lee, K. C., Kim, J. E., Jung, K. Y., Ahn, J. M., Kim, H.G., Lee, I. J., Shin, D. S., & Yang, D. S. (2017). Change ingeochemical characteristics of surface sediments in theNakdong river main stream. Journal of EnvironmentalScience International, 26(3), 311–324. https://doi.org/10.5322/JESI.2017.26.3.311.

Kim, S., Ahn, J. M., Kim, H. G., Kwon, H. G., Kim, G. H., Shin,D. S., & Yang, D. S. (2018). The distribution characteristicsof grain size and organic matters of surface sediments fromthe Nakdong-Goryeong mid-watershed. Journal ofEnvironmental Science International, 27(6), 411–423.https://doi.org/10.5322/JESI.2018.27.5.281.

Lai, T. M., Lee, W., Hur, J., Kim, Y., Huh, I. A., Shin, H. S., &Lee, J. H. (2013). Influence of sediment grain size and landuse on the distributions of heavy metals in sediments of theHan River Basin in Korea and the assessment of anthropo-genic pollution.Water, Air, and Soil Pollution, 224(7), 1609–1621. https://doi.org/10.1017/s11270-013-1609-y.

Lee, J. B., Hong, S. H., Kim, D. H., Huh, I. A., Huh, Y. J., Khan, J.B., Oh, D. Y., Kim, K. Y., Lee, Y. J., Lee, S. H., & Shin, H.S. (2014). Measurement of metals in sediment of the Geum-River and their correlation. Analytical Science& Technology,27(1), 11–24. https://doi.org/10.5806/AST.2014.27.1.11.

Md, S. I., Md, K. A., Mohammad, R., Md, H. A. M., &Muhammad, K. I. (2015). Heavy metal pollution in surfacesediment: A preliminary assessment of an urban river in adeveloping country. Ecological Indicators, 48, 282–291.https://doi.org/10.1016/j.ecolind.2014.08.016.

Ministry of Environment. (2012). Official test methods of waterquality (report no. 2012-99). Sejong: Ministry ofEnvironment.

Muller, G. (1979). Heavy metals in the sediment of the Rhine–changes seity. Umschau in Wissenschaft und Technik, 79,778–783.

NIER (2011). Baseline concentrations of heavy metals in riversediments in Korea (report no. TRKO201300007691).Inchun: National Institute Environmental Research.

NIER (2015). Sediment pollution evaluation standard of river andlake (published rulings 2015-687). Inchun: National InstituteEnvironmental Research.

NIER (2017). Result on target water quality monitoring networkoperation of Nakdong River System in 2017 (report no. 11-1480000-0001481-14). Inchun: National InstituteEnvironmental Research.

Oh, K. H., Kim, J. Y., Koh, Y. K., Youn, S. T., Shin, S. E., Park,B. Y., Moon, B. C., & Kim, H. G. (2003). Geochemicalcharacteristics and contamination of surface sediment in

https://doi.org/10.1016/j.envpol.2016.09.008https://doi.org/10.1306/74D70646-2B21-11D7-8648000102X1865Dhttps://doi.org/10.1306/74D70646-2B21-11D7-8648000102X1865Dhttps://doi.org/10.1016/j.ijsec.2018.02.004https://doi.org/10.1016/0025-326X(90)90594-Xhttps://doi.org/10.4217/OPR.2016.38.3.171https://doi.org/10.4217/OPR.2016.38.3.171https://pdfs.semanticscholar.org/bar12/56aa9766c0d3e0b6ea73bb1fa9561fbcf120.pdfhttps://pdfs.semanticscholar.org/bar12/56aa9766c0d3e0b6ea73bb1fa9561fbcf120.pdfhttps://pdfs.semanticscholar.org/bar12/56aa9766c0d3e0b6ea73bb1fa9561fbcf120.pdfhttps://doi.org/10.1016/i.marpolbul.2007.02.013https://doi.org/10.1016/i.marpolbul.2007.02.013https://doi.org/10.1016/j.eti.2015.12.001https://doi.org/10.7837/kosomes.2014.20.4.358https://doi.org/10.7837/kosomes.2014.20.4.358https://doi.org/10.7837/kossmes.2013.19.4.303https://doi.org/10.5322/JESI.2015.24.8.969https://doi.org/10.15681/KSWE.2015.31.3.304https://doi.org/10.15681/KSWE.2015.31.3.304https://doi.org/10.5322/JESI.2017.26.3.311https://doi.org/10.5322/JESI.2017.26.3.311https://doi.org/10.5322/JESI.2018.27.5.281https://doi.org/10.1017/s11270-013-1609-yhttps://doi.org/10.5806/AST.2014.27.1.11https://doi.org/10.1016/j.ecolind.2014.08.016


streams of Gwangju city. Journal of the Korean EarthScience Society, 24(4), 346–360.

Pandey, M., Tripathi, S., Pandey, A. K., & Tripathi, B. D. (2014).Risk assessment of metal species in sediments of the riverganga. Catena, 122, 140–149. https://doi.org/10.1016/j.catena.2014.06.012.

Park, J. E., Kim, J. G., & Ahn, S. Y. (2011). Distribution andsource identification of PCDD/Fs and Co-PCBs in sedimentsfrom the Geum river. Korea Environmental EngineeringResearch, 33 , 900–906. https: / /doi .org/10.4491/KSEE.2011.33.12.900.

Ra, K. T., Kim, E. S., Kim, J. K., Lee, J. M., & Kim, E. Y. (2013).Distribution and pollution assessment of trace metals in coresediments from the artificial lake Shihwa. Journal of OceanPolar Research, 35(2), 69–83. https://doi.org/10.4217/OPR.2013.35.2.069.

Sekabira, K., Oryem Origa, H., Basamba, T. A., Mutumba, G., &Kakudidi, E. (2010). Assessment of heavy metal pollution inthe urban stream sediments and its tributaries. InternationalJournal of Environmental Science and Technology, 7(3),435–446. https://doi.org/10.1007/BF03326153.

Shin, S. E., Youn, S. T., Koh, Y. K., & Oh, K. H. (2015). Thegeochemical characteristics and environmental changes ofsurface sediment in Yeonsan River area. Journal of theKorean Geomorphological Association, 22(3), 31–42.https://doi.org/10.16968/JKGA.22.3.31.

Song, Y., Ji, J., Yang, Z., Yuan, X., Mao, C., Frost, R. L., &Ayoko, G. A. (2011). Geochemical behavior assessment and

apportionment of heavy metal contaminants in the bottomsediments of lower reach of Changjiang River. Catena,85(1), 73–81. https://doi.org/10.1016/j.catena.2010.12.009.

Thornton, I. (1983). Applied environmental geochemistry.London: Academic Press.

Tomlinson, D. L., Wilson, J. G., Harris, C. R., & Jeffrey, D. W.(1980). Problems in the assessment of heavy metals in estu-aries and the formation pollution index. Helgoland ScientificMeeresunteruchung, 3(1–4), 566–575. https://doi.org/10.1007/BF02414780.

US EPA (1999). Protocol for developing sediment TMDL (reportEPA 841-B-99-004). Washington: US EPA. https://purl.access.gpo.gov/GPO/LPS50983.

Yongming, H., Peixuana, D., Junji, C., & Posmentier, E. S.(2006). Multivariate analysis of heavy metal contaminationin urban dust of Yi’an, central China. Science of the TotalEnvironment, 355, 176–186.

Zhang, J. H., & Liu, C. L. (2002). Riverine composition andestuarine geochemistry of particle metals in China-Weathering features, anthropogenic impact and chemicalfluxes. Estuarine, Coastal and Shelf Science, 54(6), 1051–1070. https://doi.org/10.1006/ecss.2001.0879.

Publisher’s note Springer Nature remains neutral with regard tojurisdictional claims in published maps and institutionalaffiliations.

https://doi.org/10.1016/j.catena.2014.06.012https://doi.org/10.1016/j.catena.2014.06.012https://doi.org/10.4491/KSEE.2011.33.12.900https://doi.org/10.4491/KSEE.2011.33.12.900https://doi.org/10.4217/OPR.2013.35.2.069https://doi.org/10.4217/OPR.2013.35.2.069https://doi.org/10.1007/BF03326153https://doi.org/10.16968/JKGA.22.3.31https://doi.org/10.1016/j.catena.2010.12.009https://doi.org/10.1007/BF02414780https://doi.org/10.1007/BF02414780https://purl.access.gpo.gov/GPO/LPS50983https://purl.access.gpo.gov/GPO/LPS50983https://doi.org/10.1006/ecss.2001.0879

Distribution of metal contamination and grain size in the sediments of Nakdong River, KoreaAbstractIntroductionMaterial and methodsStudy area and sediment samplingAnalysis of surface sedimentsSediment quality guidelinesCalculation methods of EF, Igeo, and PLIPrincipal component analysis

Result and discussionDistribution of grain sizeContamination and concentration of metalPrincipal component analysis

ConclusionsReferences

distribution of metal contamination and grain size in the ... · analysis of surface sediments the...

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